What is OEE? Defining Overall Equipment Effectiveness, Implementation, and Benchmarks

OEE (overall equipment effectiveness) is one of the best tools for optimizing a production system.

For a large-scale repetitive process, minor improvements can make a major difference. In manufacturing, shaving a few seconds off one step can mean tens of thousands of dollars in improved production every month.

Intrigued? We’ll tell you all about it and give you a road map to start using it at your organization.

What is OEE?

OEE stands for “Overall Equipment Effectiveness.” OEE is a key performance indicator (KPI) that compares your system’s ideal performance to its real performance. 

It is a quantifiable (i.e., uses numbers) way to find out how well your equipment, people, and processes do their job by measuring:

  • available time/uptime (availability)
  • production speed and consistency (performance)
  • number of defects (quality)

OEE uses this performance data to find the percentage of good production time on an asset. That means each piece of equipment gets its own OEE score. 

While scoring each machine may sound time-consuming, it is worth the effort because it also takes into account the humans that run them. You know as well as we do that machines aren’t always the problem. Staff and processes are just as likely to lower productivity. 

The Concept of Perfect Production

Your new machines are in top working order and never break. Your staff is well-trained, never late, and never requires breaks. You only produce one kind of product on all shifts, ever. Once a process is set, it is followed 100% of the time. 

That kind of perfection is impossible (and it is also kind of creepy in that sci-fi sort of way). There will be changeovers, defects, breakdowns, and missed steps. A hot and humid day will make something – or someone – overheat. And those are just minor issues.

OEE exists to answer some of the most confounding questions about production efficiency. 

  • How do you know when you’re doing enough to be as efficient as you can be? 
  • How much deviation from that sci-fi level of productivity is OK? 
  • And when you stray too far from your ideal productivity, how do you even begin to find the root cause so that you may address it?

What performance data do I need for OEE?

If you are reading this post and brushing up on a concept like OEE, chances are you already do some reporting. If not, consider this a sign to get started collecting equipment performance and maintenance data

Below is a list of metrics needed for finding an asset’s OEE. The numbers you need will fall into two main categories: parts and time. 

Measuring the number of parts you have produced

  • Good count – the number of good parts (that meet quality standards the first time) made during a set period.
  • Total count – the number of all parts (including defects) made during a set period.
  • Defective count – The number of defective parts (rejected because they do not meet quality standards) made during a set period.

Measuring production time

  • Planned production time – total time a piece of equipment is expected and scheduled to run.
  • Run time – the amount of time a process is running. Run time does not include downtime but does include small stops, slowed production, or time spent addressing rejected parts. 
  • Stop time – the total time production was stopped due to both unplanned (equipment failures, material shortages) and planned stops (changeovers, make-ready events).

Factors involved in an OEE score

The above data will eventually come together to represent three productivity factors. 

  • Availability – the amount of time your equipment is running as it should, as a percentage of planned production time that is spent running.
  • Performance – the speed of production and its consistency, represented by the percentage of how close your run time was to the ideal.
  • Quality –  the quality of parts and frequency of defects, represented by the percentage of all parts made that met quality standards.

Once you have your data on parts and time, you can plug them into the OEE formula to calculate these three factors and, ultimately, your OEE score. 

Origins of OEE in manufacturing

Overall Equipment Effectiveness is a measurement used in Total Productive Maintenance (TPM) programs. It is also a common feature of lean manufacturing, which is all about being as efficient as possible with your resources.

However, since we know that 100% productivity is unreachable, OEE gives you a way to assess how close your process is to the ideal and gives you direction on how to improve:

  1. It helps you break down where issues happen so that you can fix them more easily. 
  2. It is a diagnostic tool for your production process.
  3. It quickly uncovers losses as well as highly productive areas.
  4. It helps nudge you ever closer to your highest level of productivity.

OEE is also an instrument that helps you identify which of the common six big losses in manufacturing may be impacting your business the most.

Implementing OEE at your organization

It is always a good idea to pilot any major process changes. Pilots help you find and fix problems before they cause problems all over the place. They also help you understand how these changes will impact your organization. OEE is no different.

  • Define the scope of your pilot. Select a production area, piece of equipment, or team that is eager to improve. This will be your pilot group.
  • Determine the timeframe for your pilot. It is essential to collect enough pilot data – and the right data – to develop an accurate OEE score. Follow the definitions of each metric closely.
  • Analyze and improve. Perform your OEE analysis, pinpoint the scores that can be improved, and choose one or two to implement improvements.
  • Analyze again. After you have put changes in place, continue your data collection. Recalculate your OEE and see how it has changed.

Once your pilot is over, apply learnings and then implement it broadly. Remember that data collection is constant, and continuous improvement is never done.

For that reason, your data collection processes must be automated or built-in to day-to-day functions. Soon, you will have your first organization-wide OEE score!

OEE Implementation best practices

When getting started with OEE, there are a few best practices you should follow to ensure success.

Start from good

Overall equipment effectiveness takes your manufacturing operation from good to great – so you need to be starting from “good.” This means:

If you are in maintenance, these may not directly be part of your job. You must work with your production lines and managers. They will have access to the data you need and be partners for you in fixing issues once you find them.

You, in turn, will also be a partner to them. Your maintenance program must also be in a good place, with well-oiled preventive maintenance and tools like Limble to help with data collection. 

If you’re not there yet, take a pause on OEE to address these issues first.

Play the long-game

There are many benefits of OEE. But many avoid it because of the effort it takes to track and gather the necessary data over time. 

If you do mid- or large-scale manufacturing, you will be glad you gave it the time and effort. Saving two seconds here and three defects per hour there will make a major impact on your profitability that will only grow over time.

Track all the variables 

OEE measures the machine’s productivity, yes. But it also takes into account the humans that run them as well. 

You know as well as we do that machines are not always the problem. Staff and processes are just as likely to cause issues and reduce productivity. Gaining insights into all parts of your strategy is what OEE is all about, so you can:

  • Squeeze every drop out of your equipment
  • Reduce the number of defective products
  • Maximize workforce productivity
  • Reduce repair costs by noticing problems early
  • Eliminate wasteful steps in your production process
  • Reach production efficiency

Once you start on the path toward those improvement  initiatives, you will be well on your way to a lean manufacturing process.

In large-scale production, you need to dig deeper to go further. 

Training and tracking OEE

Tracking OEE and training staff to use it are both vital elements of any manufacturing process worth its salt. If your organization lacks either one, you may find it challenging to put OEE in place. 

Make sure your organization understands the value of these two topics and gives the resources needed for them to be done well. 

Limble CMMS is easy to use and tracks all kinds of data. It also offers templates and checklists for maintenance staff and maintenance operators to support your training programs.

Common OEE mistakes and how to avoid them

Overall Equipment Effectiveness can take your manufacturing process to the next level. However, if not done correctly, you may not reap the full benefits. Here are some of the most common mistakes with OEE.

Focusing on the OEE score, not the losses 

Just like your bank account balance, you can stare at your OEE score all day long and it won’t get any bigger until you roll up your sleeves, get to work, and earn a paycheck. 

OEE is a great way to measure where you are at any given point. But if you want to improve, the real focus must be on the losses and the steps you take to minimize them. 

Using OEE to compare unrelated processes and plants

Going from 0 to 60 MPH in 3 seconds in a Ferrari is expected – that’s what it was built for. But doing the same thing in your Toyota Prius is downright impossible. They are different machines with very (very) different purposes. Comparing them minimizes the value of both vehicles.

It is tempting to compare OEE scores across your organization but do so with caution. You must consider each individual process and its purpose.

Excluding changeovers in your time data

Yes, changeovers cost production time. There is no way around it. But they are also essential parts of your business and ripe areas for improvement. 

Excluding them not only minimizes the accuracy of your score, but it also deprives you of one of your most profitable opportunities for improvement and impact to your bottom line. 

Implementing OEE across the whole plant 

Woah there, Nelly. It can be exciting to have a tool that boils down your productivity into one easy-to-understand number. You may be eager to use it everywhere right out of the gates. 

Remember that it does take a good amount of time and effort to do it correctly. By piloting the program, you can find areas where OEE will be most helpful and those where it will not.

Your data collection is too slow

Data collection that drags on and on runs the risk of spanning changes in process, staff, seasons, etc. All these things can mess with your data and make it hard to know what exactly needs improvement.

Collect data thoroughly and swiftly so that you obtain a score that truly represents your production process. A CMMS and other cloud-based data automation technology can help immensely.

Doing OEE “your way”

OEE is most valuable when compared across similar teams, production lines, and even similar organizations and industries. 

But, when you change the rules and evaluate efficiency your own way, you lose the ability to benchmark yourself, as well as the benefit of quickly finding and making improvements.

OEE industry benchmarks and standards

After implementation, it is important to understand what the scores you find actually mean. That context can be provided by two things: your own internal baseline, and industry benchmarks. 

First baseline

Your first OEE findings will serve as your baseline, the benchmark to which you compare all future scores. 

It will be the starting point with which you compare OEEs and measure improvements. As you make those improvements and comparisons, keep in mind:

  • How much data did you include in this baseline? Measurement over a longer period gives a more accurate score.
  • What part of the production process was included? Consider comparing OEE scores of different shifts or machines.

What the number itself means

OEE scores are always percentages no matter what they are measuring. They were designed this way so that they are easier to compare. This helps you know how your OEE stacks up to others – both inside and outside your company.

We can see that 85% is a world-class OEE score. A score of 60% is very common and implies that there is room for substantial improvement. And 40% OEE is typical for those just starting on their continuous improvement journey. 

The key takeaway here is that getting your first OEE score is just the beginning. Your OEE is a KPI that can not only tell you where you stand, but it can also tell you what direction to go in to improve.

You’re in the Major Leagues now

Excellent maintenance and manufacturing teams practice consistency, leverage the right tools, and use best practices for continuous improvement. 

They also take every opportunity they can to reduce waste and use as few resources as possible. OEE is an excellent tool for this because it helps you:

  • Collect valuable data on maintenance operations and production, helping you take a big-picture approach.
  • Use proven assessments and analysis to peel back the layers and uncover ways to improve.

Limble is your partner in achieving world-class OEE and many other efficiency strategies. We offer an easy-to-use platform that enables each step in the OEE process. You can start a free trial here, request a demo, or even try out our online self-demo.

  • Thanks for good information

    What is diffrenece between OEE and mtbf also mttr .

    Which ismuch more effective .

    And how about Reliability ?


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  • for performance section:
    plant XYZ produces 3600 for 60 min
    then the planned production time is 240 min
    The total count should be (3600/60)*240 = 14400 right?
    How did you get 14100 ?

    I’ll try with my manufacturing and i get my performance as 100%. Can you help me?

    Thank you.

  • Hi,

    In short, 3600 units in 60 min is in ideal conditions (what the machine manual would say the machine can do when it is brand new).

    We randomly selected a lower number of the total actual produced units that represents a more realistic scenario where conditions are not ideal. In other words, in that example, we imagine that wear and tear or poor quality of the input material slows down the production a tiny bit so the total number of produced units is less than theoretically possible.

    Maybe the text didn’t explain that point in the clearest way, I hope that this clears things up a bit.

  • Okay, thank you for the explanation.
    I want to ask one question. I want calculate OEE for my machines. So, how can I randomly chooses any number for the total count. The calculation for performance is 100%. Is it okay? As I don’t randomly choose lower number as you do.

  • You definitely should not take a random number 🙂 If possible, you should measure the actual output and that will be your total count. Then you can see if that actual output was the same as maximally possible output you defined earlier.

  • You need to calculate the downtime of a machine first on a daily basis.
    Downtime will include 4 losses of availability i.e. Shutdown loss, Production adjustment loss, equipment failure loss, and process failure loss.
    Then deduct these loss timing from planned production time( the result will run time). Now calculate availability as (run time/planned prod. time).
    Then calculate performance as {(ideal cycle time*total count)/run time}.
    At last, calculate Quality as (good count/total count).
    calculating OEE = Availability*Performance*quality

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